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An algorithm of improved AO* based on discrete binary particle swarm optimization (DBPSO) with additional random item is proposed, which can solve the Optimal Test-sequencing Problem (OTP) in large-scale complicated electron system. DBPSO optimizes the test sets which can isolate the expanded node in AO* algorithm to decrease the number of node. The result of real operation show that this algorithm...
The paper introduces the random factor in Particle Swarm Optimization. Comparing with inertia weight, the particle's velocity is determined by previous velocity, own experience, public knowledge and random behavior. The random operator is similar with the mutation operator in the Genetic Algorithms. Simulation results show that the method introducing the random factor is better than inertia weight...
Analyzing the distance between the location and the new location, we conclude inertia weight method which linearly decreases from 0.9 to 0.4 has the powerful local search ability on Schafferpsilas F6 function. In order to improve the balance between local and global search ability, the novel adaptive PSO algorithm which evaluates a reset function to control the inertia weight value is proposed. Once...
In order to search better solution in the high dimension space, the novel hybrid PSO-BP algorithm which combines the PSO mechanism with the Levenberg-Marquardt algorithm or the conjugate gradient algorithm is proposed. The main idea employs BP algorithm with numeric technology to find the local optimum, and takes the weights and biases trained as particles, and harnesses swarm motion to search the...
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